The present work focuses on the prediction of the hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. The data considered focus on a novel hot forming process at different tool temperatures ranging from $24{}^{\circ }$C to $350{}^{\circ }$C to set different cooling rates after solution heat-treatment. Isothermal uniaxial tensile tests in the temperature range of $200{}^{\circ }$C to $400{}^{\circ }$C and at strain rates ranging from 0.001 s${}^{-1}$ to 0.1 s${}^{-1}$ were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the Zerilli–Armstrong model (Z–A) as reference. Related work focuses on predicting single data points of the curves that the model was trained on. Due to the way data were split with respect to training and testing data, it is possible to predict entire stress–strain curves. The model allows to decrease the number of required laboratory experiments, eventually saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z–A model, the extreme Gradient Boosting model (XGB) showed superior results, i.e., the highest error reduction of 91% with respect to the Mean Squared Error.

Laser-based powder bed fusion of metals (PBF-LB/M) is a widely applied additive manufacturing technique. Thus, PBF-LB/M represents a potential candidate for the processing of quenched and tempered (Q&T) steels such as 42CrMo4 (AISI 4140), as these steels are often considered as the material of choice for complex components, e.g., in the toolmaking industry. However, due to the presence of process-induced defects, achieving a high quality of the resulting parts remains challenging in PBF-LB/M. Therefore, an extensive quality inspection, e.g., using process monitoring systems or downstream by destructive or non-destructive testing (NDT) methods, is essential. Since conventionally used downstream methods, e.g., X-ray computed tomography, are time-consuming and cost-intensive, micromagnetic NDT measurements represent an alternative for ferromagnetic materials such as 42CrMo4. In this context, 42CrMo4 samples were manufactured by PBF-LB/M with different process parameters and analyzed using a widely established micromagnetic measurement system in order to investigate potential relations between micromagnetic properties and porosity. Using multiple regression modeling, relations between the PBF-LB/M process parameters and six selected micromagnetic variables and relations between the process parameters and the porosity were assessed. The results presented reveal first insights into the use of micromagnetic NDT measurements for porosity assessment and process parameter optimization in PBF-LB/M-processed components.

Fracture surface analysis is of utmost importance with respect to structural integrity of metallic materials. This especially holds true for additively manufactured materials. Despite an increasing trend of automatization of testing methods, the analysis and classification of fatigue fracture surface images is commonly done manually by experts. Although this leads to correct results in most cases, it has several disadvantages, e.g., the need of a huge knowledge base to interpret images correctly. In present work, an unsupervised tool for analysis of overview images of fatigue fracture surface images is developed to support nonexperienced users to identify the origin of the fracture. The tool is developed using fracture surface images of additively manufactured Ti6Al4V specimens fatigued in the high‐cycle‐fatigue regime and is based on the identification of river marks. Several recording parameters seem to have no significant influence on the results as long as preprocessing settings are adapted. Moreover, it is possible to analyze images of other materials with the tool as long as the fracture surfaces contain river marks. However, special features like multiple origins or origins located in direct vicinity to the surface, e.g., caused by increased plastic strains, require a further tool development or alternative approaches.

Process monitoring systems, e.g., systems based on photodiodes, could be used in laser-based powder bed fusion (PBF-LB/M) to measure various process parameters and process signatures to eventually allow for a local, detailed analysis of the produced parts. Here, simple statements only concerning the occurrence of defects in parts are sufficient in many cases, especially with respect to industrial application. Therefore, a pragmatic approach to rapidly infer the occurrence of defects and their types based on in situ data obtained by commercially available process monitoring systems is introduced. In this approach, a color distribution in form of a histogram is determined for each produced part using layer-wise screenshots of the visualized data provided by the monitoring software. Assessment of the histograms of AlSi10Mg samples, which were processed with different parameter combinations, revealed characteristics depending on the prevailing defect types. These characteristics enable the prediction of the occurring defect types without the necessity to apply conventional downstream testing methods, and thus, a straightforward separation of parts with good quality from defective components. Since the approach presented uses the data visualization of the monitoring software, it can be used even when direct access to the raw data is not provided by the machine manufacturer.